Eventually consistent data is a buzzword nowadays, especially in NoSQL discussions. For those not versed in tech talk, having eventually consistent data means you’re willing to sacrifice data consistency in order to gain in other areas. But most projects don't need eventually consistent data from the beginning.

This article, over a year old but reinvigorated by the recent "never use MongoDB" incident and its aftermath, takes an interesting look at last year's wave of MongoDB criticism by tracking the various arguments that had been made and responding to them individually.

Anybody looking for an alternative NoSQL solution might be interested in ArangoDB, a not-quite-new but lesser-known NoSQL database that supports key-value documents, property graphs, and works with a query language called AQL based on the syntax of XQuery, among other things.

The NoSQL Matters conference took place in Barcelona on November 30th, and now you can find slides from the presenters all collected in one place. The presentations collected cover a wide range of topics: Redis, MongoDB, DynamoDB, Riak, Neo4j, and more, including topics discussing NoSQL as a whole.

At its core, Redis is an in-memory key-value datastore. Its simple data structures and intuitive API make Redis a true power-horse for solving various ‘Big Data’-related problems. To illustrate this point, I reimplemented my MongoDB-based molecular similarity search through Redis and its integrated Lua support.

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